Things Every Programmer Should Know: #1, #2, #3

Here are the 97 Things Every Programmer Should Know project, pearls of wisdom for programmers collected from leading practitioners, published by O'Reilly (license link).

The collection is intended simply to contain multiple and varied perspectives on what it is that contributors to the project feel programmers should know. This can be anything from code-focused advice to culture, from algorithm usage to agile thinking, from implementation know-how to professionalism, from style to substance, etc.

So, let's start them, without further ado...

1. Act with Prudence by Seb Rose

"Whatever you undertake, act with prudence and consider the consequences" Anon

No matter how comfortable a schedule looks at the beginning of an iteration, you can't avoid being under pressure some of the time. If you find yourself having to choose between "doing it right" and "doing it quick" it is often appealing to "do it quick" on the understanding that you'll come back and fix it later. When you make this promise to yourself, your team, and your customer, you mean it. But all too often the next iteration brings new problems and you become focused on them. This sort of deferred work is known as technical debt and it is not your friend. Specifically, Martin Fowler calls this deliberate technical debt in his taxonomy of technical debt, which should not be confused with inadvertent technical debt.

Technical debt is like a loan: You benefit from it in the short-term, but you have to pay interest on it until it is fully paid off. Shortcuts in the code make it harder to add features or refactor your code. They are breeding grounds for defects and brittle test cases. The longer you leave it, the worse it gets. By the time you get around to undertaking the original fix there may be a whole stack of not-quite-right design choices layered on top of the original problem making the code much harder to refactor and correct. In fact, it is often only when things have got so bad that you must fix it, that you actually do go back to fix it. And by then it is often so hard to fix that you really can't afford the time or the risk.

There are times when you must incur technical debt to meet a deadline or implement a thin slice of a feature. Try not to be in this position, but if the situation absolutely demands it, then go ahead. But (and this is a big BUT) you must track technical debt and pay it back quickly or things go rapidly downhill. As soon as you make the decision to compromise, write a task card or log it in your issue tracking system to ensure that it does not get forgotten.

If you schedule repayment of the debt in the next iteration, the cost will be minimal. Leaving the debt unpaid will accrue interest and that interest should be tracked to make the cost visible. This will emphasize the effect on business value of the project's technical debt and enables appropriate prioritization of the repayment. The choice of how to calculate and track the interest will depend on the particular project, but track it you must.

Pay off technical debt as soon as possible. It would be imprudent to do otherwise.

2. Apply Functional Programming Principles by Edward Garson

Functional programming has recently enjoyed renewed interest from the mainstream programming community. Part of the reason is because emergent properties of the functional paradigm are well positioned to address the challenges posed by our industry's shift toward multi-core. However, while that is certainly an important application, it is not the reason this piece admonishes you to know thy functional programming.

Mastery of the functional programming paradigm can greatly improve the quality of the code you write in other contexts. If you deeply understand and apply the functional paradigm, your designs will exhibit a much higher degree of referential transparency.

Referential transparency is a very desirable property: It implies that functions consistently yield the same results given the same input, irrespective of where and when they are invoked. That is, function evaluation depends less — ideally, not at all — on the side effects of mutable state.

A leading cause of defects in imperative code is attributable to mutable variables. Everyone reading this will have investigated why some value is not as expected in a particular situation. Visibility semantics can help to mitigate these insidious defects, or at least to drastically narrow down their location, but their true culprit may in fact be the providence of designs that employ inordinate mutability.

And we certainly don't get much help from industry in this regard. Introductions to object orientation tacitly promote such design, because they often show examples composed of graphs of relatively long-lived objects that happily call mutator methods on each other, which can be dangerous. However, with astute test-driven design, particularly when being sure to "Mock Roles, not Objects", unnecessary mutability can be designed away.

The net result is a design that typically has better responsibility allocation with more numerous, smaller functions that act on arguments passed into them, rather than referencing mutable member variables. There will be fewer defects, and furthermore they will often be simpler to debug, because it is easier to locate where a rogue value is introduced in these designs than to otherwise deduce the particular context that results in an erroneous assignment. This adds up to a much higher degree of referential transparency, and positively nothing will get these ideas as deeply into your bones as learning a functional programming language, where this model of computation is the norm.

Of course, this approach is not optimal in all situations. For example, in object-oriented systems this style often yields better results with domain model development (i.e., where collaborations serve to break down the complexity of business rules) than with user-interface development.

Master the functional programming paradigm so you are able to judiciously apply the lessons learned to other domains. Your object systems (for one) will resonate with referential transparency goodness and be much closer to their functional counterparts than many would have you believe. In fact, some would even assert that the apex of functional programming and object orientation are merely a reflection of each other, a form of computational yin and yang.

3. Ask "What Would the User Do?" (You Are not the User) by Giles Colborne

We all tend to assume that other people think like us. But they don't. Psychologists call this the false consensus bias. When people think or act differently to us, we're quite likely to label them (subconsciously) as defective in some way.

This bias explains why programmers have such a hard time putting themselves in the users' position. Users don't think like programmers. For a start, they spend much less time using computers. They neither know nor care how a computer works. This means they can't draw on any of the battery of problem-solving techniques so familiar to programmers. They don't recognize the patterns and cues programmers use to work with, through, and around an interface.

The best way to find out how users think is to watch one. Ask a user to complete a task using a similar piece of software to what you're developing. Make sure the task is a real one: "Add up a column of numbers" is OK; "Calculate your expenses for the last month" is better. Avoid tasks that are too specific, such as "Can you select these spreadsheet cells and enter a SUM formula below?" — there's a big clue in that question. Get the user to talk through his or her progress. Don't interrupt. Don't try to help. Keep asking yourself "Why is he doing that?" and "Why is she not doing that?"

The first thing you'll notice is that users do a core of things similarly. They try to complete tasks in the same order — and they make the same mistakes in the same places. You should design around that core behavior. This is different from design meetings, where people tend to be listened to for saying "What if the user wants to...?" This leads to elaborate features and confusion over what users want. Watching users eliminates this confusion.

You'll see users getting stuck. When you get stuck, you look around. When users get stuck, they narrow their focus. It becomes harder for them to see solutions elsewhere on the screen. It's one reason why help text is a poor solution to poor user interface design. If you must have instructions or help text, make sure to locate it right next to your problem areas. A user's narrow focus of attention is why tool tips are more useful than help menus.

Users tend to muddle through. They'll find a way that works and stick with it no matter how convoluted. It's better to provide one really obvious way of doing things than two or three shortcuts.

You'll also find that there's a gap between what users say they want and what they actually do. That's worrying as the normal way of gathering user requirements is to ask them. It's why the best way to capture requirements is to watch users. Spending an hour watching users is more informative than spending a day guessing what they want.

3 comments:

It appears that you have ripped off the contributions at http://programmer.97things.oreilly.com without correctly attributing the original work or following the terms of the Creative Commons Attribution under which contributions to the 97 Things Every Programmer Should Know site and book are licensed. Please either amend all the affected posts with the correct attribution or remove them.

Thank you Kevlin for telling me about this - I didn't do it on ourpose, but more being eager to share with my friends these smart advices, which are alway good to know, in order to enhance the quality of any software system.

I added the link to the O'Reilly book, in the preambul of each article -- so I think now is fine, right?

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